A DATA MINING APPROACH TO REVEAL PATTERNS IN AIRCRAFT ENGINE AND OPERATIONAL DATA

机译：一种揭示飞机发动机和操作数据模式的数据挖掘方法

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Modern day aircraft engines are embedded with sensors acquiring vast amounts of data related to engine performance such as pressure, temperature, efficiency, and speed during takeoff, climb and cruise stages of flight. These performance data are stored in performance reports. In addition, the maintenance actions across the fleet, service history and observations during flight are logged into service reports and pilot logs respectively. A typical fleet-wide performance dataset can be very large. Efficient operation and maintenance of a fleet requires proper use and analysis of the available data, searching for potentially useful trends within large datasets. Such trends may allow a fleet specialist to establish engine behaviour profiles under different conditions, and may provide indication of abnormalities in engine condition, or of hazardous events. Currently, fleet specialists use various data analysis tools to identify and analyse abnormal behaviour. Data is typically obtained from multiple sources, making this is a complex challenge. To address this challenge, this paper introduces a data mining tool capable of assisting fleet specialists by searching for useful patterns in large datasets, generating reliable, timely alerts when "abnormal" patterns are identified.

机译：现代航空器发动机嵌入了传感器，获得了与发动机性能有关的大量数据，如起飞，攀爬和巡航飞行期间的压力，温度，效率和速度。这些性能数据存储在性能报告中。此外，在航班期间船队，服务历史和观察的维护动作分别登录到服务报告和导频日志。典型的舰队宽性能数据集可以非常大。舰队的高效操作和维护需要正确使用和分析可用数据，搜索大型数据集中的潜在有用趋势。这些趋势可以允许舰队专家在不同条件下建立发动机行为型材，并且可以提供发动机状态或危险事件的异常的指示。目前，舰队专家使用各种数据分析工具来识别和分析异常行为。数据通常从多个源获得，使得这是一个复杂的挑战。为了解决这一挑战，本文介绍了一种数据挖掘工具，可以通过在大型数据集中搜索有用的模式，在识别出“异常”模式时，通过在大型数据集中搜索有用的模式来协助舰队专家进行辅助专家。

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《International Conference on Condition Monitoring Machinery Failure Prevention Technologies》|2008年||共11页
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Srini Sundaram; Iain G. D. Strachan; David A. Clifton; Steve King; John Palmer;
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A DATA MINING APPROACH TO REVEAL PATTERNS IN AIRCRAFT ENGINE AND OPERATIONAL DATA

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